28,936 research outputs found
Self-Selective Correlation Ship Tracking Method for Smart Ocean System
In recent years, with the development of the marine industry, navigation
environment becomes more complicated. Some artificial intelligence
technologies, such as computer vision, can recognize, track and count the
sailing ships to ensure the maritime security and facilitates the management
for Smart Ocean System. Aiming at the scaling problem and boundary effect
problem of traditional correlation filtering methods, we propose a
self-selective correlation filtering method based on box regression (BRCF). The
proposed method mainly include: 1) A self-selective model with negative samples
mining method which effectively reduces the boundary effect in strengthening
the classification ability of classifier at the same time; 2) A bounding box
regression method combined with a key points matching method for the scale
prediction, leading to a fast and efficient calculation. The experimental
results show that the proposed method can effectively deal with the problem of
ship size changes and background interference. The success rates and precisions
were higher than Discriminative Scale Space Tracking (DSST) by over 8
percentage points on the marine traffic dataset of our laboratory. In terms of
processing speed, the proposed method is higher than DSST by nearly 22 Frames
Per Second (FPS)
Good Features to Correlate for Visual Tracking
During the recent years, correlation filters have shown dominant and
spectacular results for visual object tracking. The types of the features that
are employed in these family of trackers significantly affect the performance
of visual tracking. The ultimate goal is to utilize robust features invariant
to any kind of appearance change of the object, while predicting the object
location as properly as in the case of no appearance change. As the deep
learning based methods have emerged, the study of learning features for
specific tasks has accelerated. For instance, discriminative visual tracking
methods based on deep architectures have been studied with promising
performance. Nevertheless, correlation filter based (CFB) trackers confine
themselves to use the pre-trained networks which are trained for object
classification problem. To this end, in this manuscript the problem of learning
deep fully convolutional features for the CFB visual tracking is formulated. In
order to learn the proposed model, a novel and efficient backpropagation
algorithm is presented based on the loss function of the network. The proposed
learning framework enables the network model to be flexible for a custom
design. Moreover, it alleviates the dependency on the network trained for
classification. Extensive performance analysis shows the efficacy of the
proposed custom design in the CFB tracking framework. By fine-tuning the
convolutional parts of a state-of-the-art network and integrating this model to
a CFB tracker, which is the top performing one of VOT2016, 18% increase is
achieved in terms of expected average overlap, and tracking failures are
decreased by 25%, while maintaining the superiority over the state-of-the-art
methods in OTB-2013 and OTB-2015 tracking datasets.Comment: Accepted version of IEEE Transactions on Image Processin
Long-Term Visual Object Tracking Benchmark
We propose a new long video dataset (called Track Long and Prosper - TLP) and
benchmark for single object tracking. The dataset consists of 50 HD videos from
real world scenarios, encompassing a duration of over 400 minutes (676K
frames), making it more than 20 folds larger in average duration per sequence
and more than 8 folds larger in terms of total covered duration, as compared to
existing generic datasets for visual tracking. The proposed dataset paves a way
to suitably assess long term tracking performance and train better deep
learning architectures (avoiding/reducing augmentation, which may not reflect
real world behaviour). We benchmark the dataset on 17 state of the art trackers
and rank them according to tracking accuracy and run time speeds. We further
present thorough qualitative and quantitative evaluation highlighting the
importance of long term aspect of tracking. Our most interesting observations
are (a) existing short sequence benchmarks fail to bring out the inherent
differences in tracking algorithms which widen up while tracking on long
sequences and (b) the accuracy of trackers abruptly drops on challenging long
sequences, suggesting the potential need of research efforts in the direction
of long-term tracking.Comment: ACCV 2018 (Oral
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